Blood vessels, tracheal pathways, and the like are all hierarchical tree structures. Automatically identifying and assigning correct anatomical labels to these structures facilitates the diagnostic process for physicians and radiologists. Extant methods of automatically generating medical reports generally rely on correct identification of anatomical labels (such as coronary artery vessels, bronchial trees, and the like) and correct assignment of labels based on the identification. Diagnoses may depend on the accuracy of the identification and labeling. For example, the anatomical location of a stenosis often suggests the severity of its effect on the normal functionality of the heart. Accordingly, if a stenosis is detected by extant algorithms, it is important to identify where it is located, which may depend on automatically identifying the correct anatomical labels for the corresponding vessel (e.g., a left anterior descending artery).
As one example, anatomically meaningful coronary artery supplies a specific area of the myocardium, but the morphology and topology of these arteries vary widely on an individual basis. Therefore, one challenge of automatic labeling arises from the large individual variability of coronary anatomy, especially with regards to some of the secondary arteries emerging from the main branches. Such complexity also challenges labeling of other vessels in the human body, other than coronary arteries.
Extant methods are also not sufficiently reliable with regards to large individual variability. Generally, extant methods typically rely on a human coronary atlas model based on statistical results of a limited number of human coronary arteries or on hard coded geometrical or topological criteria and/or parameters. Due to the individual variability, such methods are less robust in labeling the vessels.
This disclosure provides a method and device that may quickly, accurately, and automatically generate anatomical labels for a physiological tree structure. The method and device increase the robustness and accuracy of automatically labeling by using learning networks (such as a neural network), which are able to learn essential anatomical characteristics without any human defined criteria and also continuously improve performance with increasing data. Besides, the method and device may select (or vary) the level of the geometrical features to be extracted and fed into the learning network, so as to satisfy the needs on labeling speed, resource consuming, and granular accuracy.